code: def test_loading_albert():
      """
      Albert is a special case because it includes a Linear layer in the encoder
      that maps the embeddings to the encoder hidden size, but we include this linear
      layer within our embedding layer.
      """
      transformer_embedding = (
          TransformerEmbeddings.from_pretrained_module(
              "albert-base-v2",
          )
      )
      albert = AutoModel.from_pretrained("albert-base-v2")
      assert_allclose(
          transformer_embedding.embeddings.word_embeddings.weight.data,
          albert.embeddings.word_embeddings.weight.data,
      )
      assert_allclose(
          transformer_embedding.linear_transform.weight.data,
          albert.encoder.embedding_hidden_mapping_in.weight.data,
      )
  
imports: import copy
  import pytest
  import torch
  from torch.testing import assert_allclose
  from transformers import AutoModel
  from transformers.models.bert.configuration_bert import BertConfig
  from transformers.models.bert.modeling_bert import BertEmbeddings
  from transformers.models.albert.configuration_albert import AlbertConfig
  from transformers.models.albert.modeling_albert import AlbertEmbeddings
  from allennlp.common import Params, FromParams
  from allennlp.modules.transformer import TransformerEmbeddings, ImageFeatureEmbeddings, TransformerModule
label_desc: Use image node_id 10 for calling a global function with example usage: test_loading_albert() without return types